Comparative Computational Assessment of Hydrocarbon Bioremediation Potential Using Catechol 2,3-Dioxygenases from Cytobacillus kochii and Marinobacter sp.
Year: 2025
Type: article
Abstract: This study explores the potential of two marine-derived bacteria, Cytobacillus kochii and Marinobacter, through in silico analysis of their catechol 2,3-dioxygenase (C23O) enzymes. Molecular docking simulations were conducted using AutoDock Vina to assess the binding interactions between C23O enzymes and ten hydrocarbon pollutants, including monocyclic and polycyclic aromatic hydrocarbons (PAHs). Binding affinities ranged from −4 to −8.7 kcal/mol for Cytobacillus kochii, with the highest affinity observed for fluoranthene (−8.7 kcal/mol), followed by pyrene (−8.5 kcal/mol) and phenanthrene (−8.2 kcal/mol). In comparison, Marinobacter’s C23O showed binding affinities between −4.1 and −8 kcal/mol, with fluoranthene (−8 kcal/mol) and phenanthrene (−7.9 kcal/mol) being top performers. Despite slightly lower affinity, Marinobacter exhibits superior environmental resilience under high salinity and temperature, making it valuable for application in fluctuating marine conditions. Structural interaction analysis revealed consistent pi-pi stacking and hydrogen bonding within the active sites, further supporting enzyme–substrate compatibility. These computational findings underscore Cytobacillus kochii ’s superior catalytic potential and Marinobacter’s ecological robustness. The integration of both strains into a microbial consortium offers a promising synergistic approach, combining enzymatic efficiency and environmental adaptability for effective hydrocarbon degradation. While these computational assessments offer valuable predictive insights, further validation through in vitro and in vivo experiments would be beneficial to determine the actual hydrocarbon degradation efficiencies.
Source: Catalysts
Institution: King Abdulaziz University
Language: English
Subfield: Pollution
Field: Environmental Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,654
Funder: King Abdulaziz University
OpenAlex ID: W4416571364